Mechanical and kinetic factors drive sorting of F-actin crosslinkers on bundles
Bibliographic record
Abstract
In cells, actin binding proteins (ABPs) sort to different regions in order to establish F-actin networks with diverse functions, including filopodia used for cell migration, or contractile rings required for cell division. Recent experimental work uncovered a passive mechanism that may facilitate spatial localization of ABPs: binding of a short crosslinker protein to two actin filaments promotes the binding of other short crosslinkers and inhibits the binding of longer crosslinkers (and vice versa). We hypothesize this sorting arises because F-actin is semiflexible and cannot bend over short distances. We develop a mathematical theory and a kinetic Monte Carlo simulation encompassing the most important physical parameters for this process, and use simulations of a coarse-grained but molecularly explicit model to characterize and test our predictions about the interplay of mechanical and kinetic parameters. Our theory and data predict an explicit dependence of crosslinker separation on bundle polymerization rate. We perform experiments that confirm a dependence on polymerization rate, but in an unanticipated non-monotonic manner. We use simulations to show that this non-monotonic behavior can arise in situations where crosslinkers have equal bundling affinity at equilibrium, but differing microscopic binding rates to filaments. This dependence of sorting on actin polymerization rate is a non-equilibrium effect, qualitatively similar to non-equilibrium domain formation in materials growth. Thus our results reveal an avenue by which cells can organize molecular material to drive biological processes, and can also guide the choice and design of crosslinkers for engineered protein-based materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".